Method, system, and computer readable medium for graphically displaying related text in an electronic document

• US 9,002,701 B2
• Filed: 09/21/2011
• Issued: 04/07/2015
• Est. Priority Date: 09/29/2010
• Status: Expired due to Fees

• Current Assignee: Concert Technology Corporation
• Original Assignee: Rhonda Enterprises, LLC (Concert Technology Corporation)
• Inventors: Kandekar, Kunal, Curtis, Scott, Svendsen, Hugh
• Primary Examiner(s): WOZNIAK, JAMES S
• Application Number:
  US13/239,363
• Publication Number:
  US 20120078613A1
• Time in Patent Office: 1,294 Days
• Field of Search: 704/1, 704/7, 704/9, 707/705, 707/708, 707/723, 707/738, 707/725
• US Class Current: 704/9
• CPC Class Codes: G06F 3/0482 Interaction with lists of selectable items, e.g. menus
• CPC Class Codes: G06F 3/04842 Selection of displayed objects or displayed text elements G06F3/0482 takes precedence
• CPC Class Codes: G06F 40/258 Heading extraction; Automatic titling; Numbering

Abstract

Disclosed herein are systems and methods for navigating electronic texts. According to an aspect, a method may include receiving search criteria for searching an electronic text. Further, the method may include determining text subgroups within the electronic text. The method may also include determining, for each text subgroup, a similarity relationship between the search criteria and the text subgroup. Further, the method may include presenting, for each text subgroup, a graphic representing the similarity relationship between the text subgroup and the search criteria.

Claims

1. A method comprising:

receiving, using a processor, an input identifying search criteria for searching an electronic text;

determining, using the processor, text subgroups within the electronic text;

selecting, using the processor, a text subgroup relating to a current user position within the electronic text and the search criteria;

determining, using the processor, a similarity relationship between the search criteria and one or more text subgroups adjacent to the selected text subgroup;

creating, using the processor, a text cluster by associating the search criteria with the one or more adjacent text subgroups based on the determined similarity relationship to the search criteria;

determining, using the processor, an activity indication of times when a user has read other text subgroups within the electronic text;

determining, using the processor, a similarity relationship between the text cluster and the other text subgroups;

linking, using the processor, the text cluster to one of the other text subgroups based on the determined similarity relationship to the one of the other text subgroups; and

presenting, using the processor, a graphic representing the similarity relationship between the one of the other text subgroups and the text cluster based on the activity indication of times when the user read the other text subgroups.

2. The method of claim 1, wherein receiving the search criteria comprises receiving identification of one of a person, place, and thing.

3. The method of claim 1, wherein receiving the search criteria comprises receiving one or more words.

4. The method of claim 1, wherein receiving the search criteria comprises receiving user input indicating the search criteria.

5. The method of claim 1, wherein determining the text subgroups comprises determining the text subgroups based on a similarity relationship of content contained within each text subgroup.

6. The method of claim 1, further comprising:

associating the graphic with the one of the other text subgroups;

receiving user input for selecting the graphic; and

in response to receiving the user input, controlling a user interface to present content of the one of the other text subgroups associated with the selected graphic.

7. The method of claim 1, wherein determining the text subgroups comprises defining the text subgroups according to one of chapters, paragraphs, and sentences of the electronic text.

8. The method of claim 1, wherein determining the text subgroups comprises defining the text subgroups according to chapters and paragraphs of the electronic text,

wherein the method further comprises:

receiving user input for selecting a graphic associated with one of a chapter and paragraph;

in response to receiving user input selecting the graphic associated with the chapter, presenting graphics representing relationships between the search criteria and paragraphs within the chapter; and

in response to receiving user input selecting the graphic associated with the paragraph, presenting graphics representing relationships between the search criteria and sentences within the paragraph.

9. The method of claim 1, wherein determining the similarity relationship between the text cluster and the other text subgroups comprises determining a similarity score between the text cluster and the other text subgroups,

wherein presenting the graphic comprises presenting the graphic as an indicator of the similarity score.

10. The method of claim 9, wherein the graphic includes a predetermined color that indicates the similarity score.

11. The method of claim 1, wherein determining the similarity relationship between the text cluster and the other text subgroups comprises one of performing semantic analysis, performing natural language processing, identifying causal relationships between the text seed and the text subgroups, determining semantic similarity based on ontology, using a semantic index to compare semantic similarities, and determining a statistic similarity.

12. The method of claim 1, wherein determining the similarity relationship between the text cluster and the other text subgroups comprises:

using a semantic graph representing relations between words to compare the search criteria to one or more words in each of one or more adjacent text subgroups; and

using the comparison to associate the search criteria with the one or more adjacent text subgroups.

13. The method of claim 12, further comprising generating the semantic graph using at least one of pre-generated summarization of the electronic text, metadata of the electronic text, semantic analysis of the electronic text, semantic indexing, natural language processing (NLP), and statistical techniques.

14. The method of claim 1, further comprising storing a navigational aid in metadata of the electronic text that indicates the similarity relationship between the text cluster and the other text subgroups.

15. An electronic device comprising:

an electronic document manager including at least a processor and memory configured to;

receive search criteria for searching an electronic text;

determine a current user position within the electronic text;

determine text subgroups within the electronic text;

select a text subgroup relating to the current user position within the electronic text and the search criteria;

determine a similarity relationship between the search criteria and one or more text subgroups adjacent to the selected text subgroup;

create a text cluster by associating the search criteria with the one or more adjacent text subgroups based on the determined similarity relationship to the search criteria;

determine an activity indication of times when a user has read other text subgroups within the electronic text;

determine a similarity relationship between the text cluster and the other text subgroups;

link the text cluster to one of the other text subgroups based on the determined similarity relationship to the one of the other text subgroups; and

a user interface configured to present a graphic representing the similarity relationship between the one of the other text subgroups and the text cluster based on the activity indication of times when the user read the other text subgroups.

16. The electronic device of claim 15, wherein the electronic document manager is configured to receive identification of one of a person, place, and thing.

17. The electronic device of claim 15, wherein the electronic document manager is configured to determine the text subgroups based on a similarity relationship of content contained within each text subgroup.

18. The electronic device of claim 15, wherein the electronic document manager is configured to:

associate the graphic with the one of the other text subgroups;

receive user input for selecting the graphic; and

control the user interface to present content of the one of the other text subgroups associated with the selected graphic in response to receiving the user input.

19. The electronic device of claim 15, wherein the electronic document manager is further configured to define the text subgroups according to one of chapters, paragraphs, and sentences of the electronic text.

20. A non-transitory computer-readable storage medium having stored thereon computer executable instructions for performing the following steps:

receiving search criteria for searching an electronic text;

determining a current user position within the electronic text;

determining text subgroups within the electronic text;

selecting a text subgroup relating to the current user position within the electronic text and the search criteria;

determining a similarity relationship between the search criteria and one or more text subgroups adjacent to the selected text subgroup;

creating a text cluster by associating the search criteria with the one or more adjacent text subgroups based on the determined similarity relationship to the search criteria;

determining an activity indication of times when a user has read other text subgroups within the electronic text;

determining a similarity relationship between the text cluster and the other text subgroups;

linking the text cluster to one of the other text subgroups based on the determined similarity relationship to the one of the other text subgroups; and

presenting a graphic representing the similarity relationship between the one of the other text subgroups and the text cluster based on the activity indication of times when the user read the other text subgroups.